Towards the development of affective facial expression recognition for human-robot interaction

Abstract

Affective facial expression is a key feature of non-verbal behavior and is considered as a symptom of an internal emotional state. Emotion recognition plays an important role in social communication: human-human and also for human-robot interaction. This work aims at the development of a framework able to recognise human emotions through facial expression for human-robot interaction. Simple features based on facial landmarks distances and angles are extracted to feed a dynamic probabilistic classification framework. The public online dataset Karolinska Directed Emotional Faces (KDEF) [12] is used to learn seven different emotions (e.g. Angry, fearful, disgusted, happy, sad, surprised, and neutral) performed by seventy subjects. Offline and on-the-fly tests were carried out: leave-one-out cross validation tests using the dataset and on-the-fly tests during human-robot interactions. Preliminary results show that the proposed framework can correctly recognise human facial expressions with potential to be used in human-robot interaction scenarios.